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Sniecinska-Cooper A.M.,Middlesex University | Iles R.K.,Eric Leonard Kruse Foundation for Health Research | Butler S.A.,Middlesex University | Jones H.,Middlesex University | And 3 more authors.
Sleep Medicine

Objective: A high rate of sleep disturbances has been reported in individuals with Williams syndrome (WS) but the underlying aetiology has yet to be identified. Melatonin and cortisol levels display circadian rhythmicity and are known to affect and regulate sleep/wake patterns. The current study examined the levels of these two endocrine markers and explored a possible relationship with sleep patterns in children with WS. Methods: Twenty-five children with WS and 27 typically developing age- and gender-matched comparison children were recruited. Saliva was collected from each child at three time points: 4-6 pm, before natural bedtime, and after awakening. The levels of salivary melatonin and cortisol were analysed by specific enzyme-linked immunoassays. Sleep patterns were examined using actigraphy and the Children's Sleep Habit Questionnaire. Results: The WS group had shallower drops in cortisol and less pronounced increase in melatonin at bedtime compared to the controls. Furthermore, they also had significantly higher levels of cortisol before bedtime. Conclusions: Increased bedtime cortisol and less pronounced rise in melatonin levels before sleep may play a role in the occurrence of sleep disturbances, such as delayed sleep onset, observed in children with WS. As both markers play a significant role in our circadian rhythm and sleep/wake cycle, it is necessary to examine sleep using multi-system analysis. © 2014 Elsevier B.V. Source

Trivedi D.K.,Eric Leonard Kruse Foundation for Health Research | Trivedi D.K.,Middlesex University | Trivedi D.K.,University of Manchester | Iles R.K.,Eric Leonard Kruse Foundation for Health Research | Iles R.K.,MAP Diagnostic Ltd
Biomedical Chromatography

In this data-rich age it is no longer necessary to methodically isolate, characterize and measure specific molecules. What is important is to identify which of the hundreds or thousands of resolved and measured 'unknown' molecules are potentially associated with the pathophysiology of interest. We have taken LC-MS data from pregnancy urine and applied SIMCA P+ data analysis software in shotgun metabolomics to search the large amount of data for significant metabolite changes that occur in the transition from the first to early second trimester of pregnancy. Seventy-two individual urine samples were examined spanning 9-23 weeks of gestation. Three-hundred and eighty-three ions were identified and variations were mapped between profiles of different gestational age and the significance quantified. In urine collected during pregnancy, the transition from first to early second trimester revealed a relatively steady pattern of metabolites except for four that showed a dramatic fall in abundance as pregnancy progressed from the first to second trimester. The pattern of changes in urinary metabolites identified by Zwitterionic Hydrophilic Liquid Interaction Chromatography (ZIC-HILIC) coupled to mass spectrometry was evaluated and we established a baseline of changes from which a search for metabolomic markers associated with clinical pathologies of pregnancy can be made as a part of wider ultraomics study. © 2014 John Wiley & Sons, Ltd. Source

Trivedi D.K.,Middlesex University | Iles R.K.,Eric Leonard Kruse Foundation for Health Research
Biomedical chromatography : BMC

In Down syndrome (DS) in particular, the precise cellular mechanisms linking genotype to phenotype is not straightforward despite a clear mapping of the genetic cause. Metabolomic profiling might be more revealing in understanding molecular-cellular mechanisms of inborn errors of metabolism/syndromes than genomics alone and also result in new prenatal screening approaches. The urinary metabolome of 122 maternal urine from women with and without an aneuploid pregnancy (predominantly Down syndrome) were compared by both zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) and reversed-phase liquid chromatography (RPLC) coupled to hybrid ion trap time of flight mass spectral analysis. ZIC-HILIC mass spectrometry resolved 10-fold more unique molecular ions than RPLC mass spectrometry, of which molecules corresponding to ions of m/z 114.07 and m/z 314.20 showed maternal urinary level changes that significantly coincided with the presence of a DS fetus. The ion of m/z 314.20 was identified as progesterone and m/z 114.07 as dihydrouracil. A metabolomics profiling-based maternal urinary screening test modelled from this separation data would detect approximately 87 and 60.87% (using HILIC-MS and RPLC-MS, respectively) of all DS pregnancies between 9 and 23 weeks of gestation with no false positives. Copyright © 2014 John Wiley & Sons, Ltd. Source

Trivedi D.K.,Eric Leonard Kruse Foundation for Health Research | Trivedi D.K.,University of Manchester | Iles R.K.,Eric Leonard Kruse Foundation for Health Research | Iles R.K.,MAP Diagnostic Ltd
Biomedical Chromatography

Metabolomics is currently being adopted as a tool to understand numerous clinical pathologies. It is essential to choose the best combination of techniques in order to optimize the information gained from the biological sample examined. For example, separation by reverse-phase liquid chromatography may be suitable for biological fluids in which lipids, proteins and small organic compounds coexist in a relatively nonpolar environment, such as serum. However, urine is a highly polar environment and metabolites are often specifically altered to render them polar suitable for normal phase/hydrophilic interaction liquid chromatography. Similarly, detectors such as high-resolution mass spectrometry (MS) may negate the need for a pre-separation but specific detection and quantification of less abundant analytes in targeted metabolomics may require concentration of the ions by methods such an ion trap MS. In addition, the inherent variability of metabolomic profiles need to be established in appropriately large sample sets of normal controls. This review aims to explore various techniques that have been tried and tested over the past decade. Consideration is given to various key drawbacks and positive alternatives published by active research groups and an optimum combination that should be used for urinary metabolomics is suggested to generate a reliable dataset for baseline studies. © 2014 John Wiley & Sons, Ltd. Source

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